A Dataset and Method for Hallux Valgus Angle Estimation Based on Deep Learing

TL;DR

This paper introduces a new dataset and a deep learning-based method for automatically estimating the Hallux Valgus angle, aiming to improve accuracy and efficiency over manual measurements in clinical settings.

Contribution

The authors created a novel dataset and developed a deep learning and linear regression approach specifically for Hallux Valgus angle estimation, addressing previous limitations in data and methodology.

Findings

High accuracy in angle estimation compared to ground truth

Effective deep learning model trained on the new dataset

Potential to automate clinical assessment of HV

Abstract

Angular measurements is essential to make a resonable treatment for Hallux valgus (HV), a common forefoot deformity. However, it still depends on manual labeling and measurement, which is time-consuming and sometimes unreliable. Automating this process is a thing of concern. However, it lack of dataset and the keypoints based method which made a great success in pose estimation is not suitable for this field.To solve the problems, we made a dataset and developed an algorithm based on deep learning and linear regression. It shows great fitting ability to the ground truth.